In image apparatuses such as cameras, camcorders, webcams, etc., an image sensor is mounted and the image sensor generally adopts a Bayer pattern configuration. The Bayer pattern refers to a pattern in which a pattern of two colors of Red (R) and Green (Gr) on a row and a pattern of two colors of Green (Gb) and Blue (B) on another row are repetitively arranged, as shown in FIGS. 1A, 1B, 1C, and 1D. As shown in FIGS. 1A, 1B, 1C, and 1D, by using a Bayer color filter, data is obtained in which only one value among Red, Green, and Blue exists in each pixel. Since Green next to Red and Green next to Blue have different sensitivities, the Bayer pattern may be divided as shown in FIGS. 1A, 1B, 1C, and 1D according to four types of center pixels, R, Gr, Gb, and B.
The size of an image sensor does not increase in spite of increase in the number of pixels of the image sensor, such that an area of each pixel relatively decreases. Such decrease in the pixel area reduces a capacity of a photodiode, degrading the sensitivity of the image sensor. To prevent this phenomenon, a recent Complementary Metal Oxide Semiconductor (CMOS) image sensor uses a share structure for increasing the capacity of the photodiode and improving the sensitivity of the image sensor. If a transistor and a drive region are shared, the form and structure of share pixels are asymmetric, causing a signal difference between the share pixels. Such a difference is expressed as noise, which is so-called a “maze effect”, after color interpolation.
To cancel the noise, noise cancellation algorithms have been studied. One of them is the most common noise cancellation method which uses a weighted mean filter. This noise cancellation method cancels noise by being applied to pixels of the same type as the center pixel. Assuming channels of Gr and Gb are the same channel, noise and errors related to channel sensitivity are removed at the same time.